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Bioinformatics for biomedicine Gene expression data, methods of analysis Lecture 7, 2006-10-31 Per Kraulis http://biomedicum.ut.ee/~kraulis Course design 1. 2. 3. 4. 5. 6. 7. 8. What is bioinformatics? Basic databases and tools Sequence searches: BLAST, FASTA Multiple alignments, phylogenetic trees Protein domains and 3D structure Seminar: Sequence analysis of a favourite gene More annotation, Gene Ontology and pathways Gene expression data, methods of analysis Seminar: Further analysis of a favourite gene Task • • • • Locate protein in GO, Reactome, etc Wee1 SREBP1 Your own Task: Wee1 • GO via – UniProt (WEE1_HUMAN) • Protein kinase; cell cycle; nucleus – Ensembl • Mitosis (code IEA: Inferred from Electronic Annotation) • Reactome – Phosphorylated by Chk1, Plk1; inactivation – Phosphorylates cyclins B1, E1, E2, A Task: SREBP1 • GO – via UniProt • ER membrane, nuclear envelope, nucleus • Transcription factor; lipid metabolism – Via Ensembl • Steroid metabolism (IEA) • Reactome: nothing • KEGG: Insulin signaling pathway – Downstream of PI3K, PIP3, PKC iota – Regulates metabolic enzymes PFK, PyK, GK Gene activity; expression • Gene expression = mRNA level • Proxy for gene activity – Approximation (usually reasonable) • Many technologies for measurement – Performance • • • • Absolute leve/relative change Accuracy Throughput (arrays vs. samples) Predefined gene set, or identification of new genes – Cost • Investment vs. running cost Microarrays for gene expression • Attach known oligonucleotides on a surface, spot by spot • Hybridize with color-labelled sample – Relative: two samples with different color – Absolute: one single color • Read off intensity of color in each spot – Convert to expression value for each gene – Relative change or absolute level cDNA microarray Pat Brown’s lab, Stanford Glass slide, cDNA spots Two-color approach: relative change Sample 1 (ref) is labelled green (Cy3) Sample 2 (exp) is labelled red (Cy5) Spot colors: • Black: no mRNA; no change • Green: exp mRNA downregulated • Red: exp mRNA upregulated • Yellow: ref and exp mRNA; no change Yeast genome DeRisi, Iyer & Brown Science 278 (1997) 680-686 Why not absolute values? • C = k * Ic – C : level of mRNA – k : proportionality constant – Ic : color intensity • Absolute value C requires k – k different for each cDNA – Calibration needed Why relative values? • How to avoid calibration for k ? • Experiment relative to reference – Measure up- or down-regulation • G = Cexp/Cref = k*Icexp/k*Icref = Icexp/Icref • But: Equal amounts of total mRNA in the two samples (exp, ref)? Data reduction issues • Treatment from raw data to useful value – From spot shape/color to up/down regulation – Similar problem in many technologies • Many steps – Depends on microarray technology – Define spot; shape, position – Measure color intensity; background? – Handle artifacts (damaged spots, etc) Normalization • Goal: Make data sets comparable • Microarrays – Between colors in chip – Between chips in experiment – Between genes in different experiments • Common approaches – Use constant gene(s); “house-keeping” genes • Ribosomal proteins • Fundamental metabolic enzymes – Danger: Based on assumptions! Example analysis: Pathways • • • • Up/down regulation of genes Map onto known pathways Indicates changes in flows or signals Mechanistic information: – Verification of known data – Patterns – Interesting anomalies • Assumes biological knowledge Yeast: diauxic shift • Green: anaerobic fermentation (glucose>ethanol) • Red: aerobic respiration (ethanol>TCA cycle) • Shows activation/ deactivation of pathways • Behaviour of gene copies DeRisi, Iyer & Brown Science 278 (1997) 680-686 Clustering 1 • Groups according to some property • Computational – Measure of relationship; distance(i,j) – Many algorithms to form groups • Powerful data analysis technique – Always some assumption on type of groups – No single optimal clustering method Clustering 2 • A set of data points • Two dimensions (x, y) • How form groups? All included Roundness Tightness Roundness All included Any shape Clustering 3 • Depends on previous knowledge – What groups are expected? – How measure when assumptions violated? Hierarchical How notice problem? Clustering 4 • Hierarchical clustering – Common in gene expression analysis – Useful, but not necessary • There is no intrinsic biological reason! • Possible problems – Sensitive to minor errors – At what level “natural” clusters? – Hard to detect “strangely shaped” clusters More than 2 dimensions • More than one treatment – Ref + exp1 + exp2 + exp3 +… • Time course experiment – Ref(t0), exp (t1), exp(t2), … • Add other parameters – Anything of interest: pI, Mw,… • Very common, and very useful Scaling problem • How to compare different dimensions? – Expression value, pI, Mw,… – Distance functions require scaling • Making values comparable • Possible approaches – Weights: Specific to each problem – Recalculation: Statistical • Same average • Same standard deviation Clustering in many dimensions • Can be generalized into N dimensions • Each point a vector (si, sj, …sn) • Distance between two points – Many possible functions; also called “metric” • Euclidean: d = sqrt(di2 + dj2 + …dn2) • Manhattan: d = |di | + |dj| + … |dn| • Correlation: similar “tendency” for values s Example: Hierarchical clustering • 12 values per gene – Time course, 0-24 hrs • Clustering – – – – Correlation coefficient metric Hierarchical; dendrogram No cutoff level No test of significance Eisen et al PNAS 95 (1998) 14863-14868 Time course experiment 1 • Yeast diauxic shift – t=9: high glucose – t=19: low glucose • Small sets of genes behaving similarly • Biological analysis – F) Ribosomal proteins – E) Several uncharacterized DeRisi, Iyer & Brown Science 278 (1997) 680-686 Time course experiment 2 • Human fibroblasts • Serum treatment • Time course; 0-24 hrs – 12 time points • Hierarchical clustering – Ordered by expression similarity Iyer, et al Science 283 (1999) 83-87 Time course experiment 3 • Human fibroblasts • Sorted according to gene function (pre-GO) Iyer, et al Science 283 (1999) 83-87 Other approaches • A set of “related” genes – Simplified clustering; in the set, or not • “Related” using any criterion: – Enriched in EST data – Cluster from gene expression – Sequence similarity – Contains a specific sequence pattern – Forms a complex –… Uncharacterized genes 1 • Goal: Identify possible classification of a previously uncharacterized gene • “Guilt by association” – If your friend is guilty, then so are you! – If gene X and Y behave similarly in an experiment, then both may be involved in the same biological process Uncharacterized genes 2 • Similar gene behavior is significant if… – Statistically significant – Some genes well-characterized, as basis – Well-designed experiment • Still, may be artifact – Spurious correlations • Price of Cuban rum vs. Swedish pharmacist salary – Indirectly related Uncharacterized genes 3 • But: Other types of correlation? – Anti-correlated – Phase-shifted – Other possibilities? • Depends on biology GOSt • Data mining tool – Gene Ontology – Start from a set of genes • E.g. co-regulated in gene expression • Any other selection criterion – Find “enriched” GO terms • Give hints for uncharacterized genes • http://www.bioinf.ebc.ee/GOST/ • Jüri Reimand, Jaak Vilo (Tartu) Data mining 1 • Given large amount of data… – Many different dimensions (types) – Many data points • How to find interesting features – Correlations – Patterns – Outliers Data mining 2 • Visualization is fundamental – Look at the data! – Look again, different angle! – Clustering, and similar, are never enough • Many dimensions – Increasingly common in clinical setting – Novel tools • Commercial: Spotfire, AVS,… • Open source: Mondrian,… Data mining 3 • Patients • Parameters – Clinical data – Biomarkers – Treatment • Spotfire visualisation Michael Merz, Novartis Presentation 2006 Spotfire web site Data mining 4 • Clustering is an example of data mining • Useful in many contexts – Gene expression – Clinical data – Text analysis; text mining • Also with smallish data sets – Example: 20 patients, 8 parameters • Found 1 clear outlier using Spotfire visualization Data mining 5 • NCI cancer drugs – 118 drugs – 60 human cancer cell lines – 8000 genes • Correlation values • Clustering Scherf et al Nature Genetics 24 (2000) 236-244 Gene expression data • Databases – Gene Expression Omnibus at NCBI http://www.ncbi.nlm.nih.gov/geo/ – ArrayExpress at EBI http://www.ebi.ac.uk/arrayexpress/ – GNF SymAtlas (Novartis Research Foundation) http://symatlas.gnf.org/SymAtlas/ • Molecular Pharmacology of Cancer (NCI) http://discover.nci.nih.gov/nature2000/